Image-to-Image
Diffusers
Safetensors
image-decomposition
layered-image-editing
diffusion
flux
lora
transparent-rgba
Instructions to use SynLayers/synlayers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SynLayers/synlayers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SynLayers/synlayers") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Upload infer/infer.yaml with huggingface_hub
Browse files- infer/infer.yaml +9 -34
infer/infer.yaml
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@@ -7,55 +7,30 @@ source_size: 1024
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target_size: 1024
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# Real-world inference defaults
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data_dir: "/
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image_dir: "/
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test_jsonl: "/
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# Model paths
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pretrained_model_name_or_path: "
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pretrained_adapter_path: "
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transp_vae_path: "ckpt/trans_vae/0008000.pt"
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# Pre-trained LoRA weights
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pretrained_lora_dir: "ckpt/pre_trained_LoRA"
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artplus_lora_dir: "ckpt/prism_ft_LoRA"
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# below is for 18k dataset
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#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_18k/step_90000/transformer"
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#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_18k/step_90000"
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#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_18k/step_90000/adapter"
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# below is for 20k dataset
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#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_20k/step_120000/transformer"
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#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_20k/step_120000"
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#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_20k/step_120000/adapter"
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# below is for 30k dataset
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#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_30k/step_150000/transformer"
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#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_30k/step_150000"
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#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_30k/step_150000/adapter"
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# below is for 40k dataset
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#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_40k/step_250000/transformer"
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#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_40k/step_250000"
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#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_40k/step_250000/adapter"
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# below is for 50k dataset
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#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_50k/step_200000/transformer"
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#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_50k/step_200000"
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#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_50k/step_200000/adapter"
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# unified real-world decomposition checkpoint
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lora_ckpt: "
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layer_ckpt: "
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adapter_lora_dir: "
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# Inference settings
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cfg: 4.0
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adapter_scale: 0.9
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max_sequence_length: 1024
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save_dir: "/
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#run_name: "step_120000" # optional manual override
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# Sample range control (1-based indexing)
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target_size: 1024
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# Real-world inference defaults
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data_dir: "demo/outputs/real_world_demo"
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image_dir: "demo/outputs/real_world_demo/layers_real_test_1024"
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test_jsonl: "demo/outputs/real_world_demo/caption_bbox_infer.jsonl"
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# Model paths
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pretrained_model_name_or_path: "SynLayers_checkpoints/FLUX.1-dev"
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pretrained_adapter_path: "SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha"
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transp_vae_path: "ckpt/trans_vae/0008000.pt"
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# Pre-trained LoRA weights
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pretrained_lora_dir: "ckpt/pre_trained_LoRA"
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artplus_lora_dir: "ckpt/prism_ft_LoRA"
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# unified real-world decomposition checkpoint
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lora_ckpt: "SynLayers_ckpt/step_120000/transformer"
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layer_ckpt: "SynLayers_ckpt/step_120000"
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adapter_lora_dir: "SynLayers_ckpt/step_120000/adapter"
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# Inference settings
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cfg: 4.0
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adapter_scale: 0.9
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max_sequence_length: 1024
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save_dir: "demo/outputs/real_world_demo/results"
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#run_name: "step_120000" # optional manual override
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# Sample range control (1-based indexing)
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